Drimva

Samsung

Staff Engineer, ML Profiling Tools

$163K–$253K

San Jose, California, United States · Staff · Full-time · Posted Oct 2, 2026

Experience
Bachelor's with 10+ years, or Master's with 8+ years, or PhD with 5+ years of industry experience.
Education
Bachelor's, Master's, or PhD.

About the job

Develops interactive web-based visual analytics and profiling tools that help engineers understand AI/ML workload performance and diagnose inference bottlenecks.

What you'll do

  • Develop and maintain large-scale web-based visual analytics tools, including visualizations of telemetry such as flame graphs, call trees, and timelines.
  • Translate complex profiling metrics into intuitive workflows and optimize the developer experience for diagnosing inference bottlenecks.
  • Collaborate with compiler engineers, hardware architects, and stakeholders to deliver profiling systems.

What they ask for

  • Bachelor's with 10+ years, Master's with 8+ years, or PhD with 5+ years of industry experience.
  • Experience designing and implementing rich, interactive data visualizations and user interfaces using modern web technologies.
  • Ability to turn large, unstructured, or multidimensional telemetry/profiling data into human-readable visualizations and design diagnostic workflows for technical users.
  • Understanding of computing architectures and performance bottlenecks; hands-on experience with or strong curiosity about systems profiling and tracing tools.

Nice to have

  • Working knowledge of machine learning frameworks such as PyTorch, JAX, or TensorFlow.
  • Knowledge of execution paradigms such as LLM inference, pipeline/tensor parallelism, or kernel dispatch.

Pay

$163,000 — $253,000 USD base salary

Tools

Perfetto, Chrome Tracing, NVIDIA Nsight Systems/Compute, PyTorch Profiler, TensorBoard, eBPF, PyTorch, JAX

AI focus

Builds profiling and visual analytics tools for AI/ML workloads, helping engineers diagnose and eliminate inference bottlenecks.

Model-written summary · the original posting is authoritative

First seen Oct 29, 2026 · Last verified Nov 5, 2026 (UTC)